The study of the optical phonon frequency of 3C-SiC by molecular dynamics simulations with deep neural network potential

2021 ◽  
Vol 129 (24) ◽  
pp. 244104
Author(s):  
Wei Chen ◽  
Liang-Sheng Li
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Optical Phonon ◽  
Deep Neural Network ◽  
Phonon Frequency ◽  
Optical Phonon Frequency ◽  
Dynamics Simulations

Neural network potential for Zr-Rh system by machine learning

2021 ◽  
Author(s):  
Kun Xie ◽  
Chong Qiao ◽  
Hong Shen ◽  
Riyi Yang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mechanical Property ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Features ◽  
Ab Initio Molecular Dynamics ◽  
The Neural Network ◽  
Dynamics Simulations ◽  
Good Agreement

Abstract Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations

2017 ◽  
Vol 19 (1) ◽  
pp. 82-96 ◽  
Author(s):  
Matti Hellström ◽  
Jörg Behler
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Coordination Polyhedra ◽  
Connectivity Patterns ◽  
Dynamics Simulations

A reactive neural network potential is used to identify coordination polyhedra and interpolyhedron connectivity patterns of NaOH solutions.

scholarly journals Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

2020 ◽  
Vol 60 (5) ◽  
pp. 2591-2604 ◽  
Author(s):  
John W. Vant ◽  
Shae-Lynn J. Lahey ◽  
Kalyanashis Jana ◽  
Mrinal Shekhar ◽  
Daipayan Sarkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Molecular Dynamics ◽  
Small Molecules ◽  
Molecular Dynamics Simulations ◽  
Dynamics Simulations

scholarly journals Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

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